Analysts spent 2 hours manually cross-referencing cloud logs per case. Now, AI agents build living models to generate reports in <5 mins.
A cybersecurity startup building an AI-native protection platform that delivers real-time detection and automated response for enterprise security teams.
Traditional security operations relied on static rule sets that quickly fell behind changing environments and produced massive investigation...
“You can't bolt intelligence onto a fundamentally static architecture. You have to start over, with AI as the reasoning engine, not an add-on.”
AI-native protection platform for security operations and automated threat detection.
Anthropic is a technology company specializing in artificial intelligence and machine learning solutions.
Artemis's Threat investigation is part of this use case:
Related implementations across industries and use cases
Devs manually seeded context for complex tasks. Agents now retain memory for weeks, working independently after a single session.
Sandboxed Python wrappers bottlenecked parsing. Claude Code migrated the 50k-line library to Go in 20 hours, doubling production speed.
Analysts spent days building reports from slow, siloed tools. Now, they ask a question and an AI agent instantly generates an accurate chart.
A small team manually investigated alerts across 35 AWS accounts. Now, engineers use AI chat to instantly pinpoint system anomalies.
Fragmented tools kept analysts chasing alerts until midnight. Now, AI agents resolve routine cases so teams work standard hours.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Surging calls caused long holds and overtime. A 24/7 AI voice agent handles routine payroll, freeing 700 HR partners for advisory work.
A 200% yearly data expansion bottlenecked global operations. Now, AI accelerates coding, drafts recipe cards, and resolves inquiries.
Legacy keyword search failed on typos and vague queries. Now, semantic AI interprets natural language and images to find exact items.
Analysts spent 2 hours manually cross-referencing cloud logs per case. Now, AI agents build living models to generate reports in <5 mins.
A cybersecurity startup building an AI-native protection platform that delivers real-time detection and automated response for enterprise security teams.
Traditional security operations relied on static rule sets that quickly fell behind changing environments and produced massive investigation...
“You can't bolt intelligence onto a fundamentally static architecture. You have to start over, with AI as the reasoning engine, not an add-on.”
AI-native protection platform for security operations and automated threat detection.
Anthropic is a technology company specializing in artificial intelligence and machine learning solutions.
Artemis's Threat investigation is part of this use case:
Related implementations across industries and use cases
Devs manually seeded context for complex tasks. Agents now retain memory for weeks, working independently after a single session.
Sandboxed Python wrappers bottlenecked parsing. Claude Code migrated the 50k-line library to Go in 20 hours, doubling production speed.
Analysts spent days building reports from slow, siloed tools. Now, they ask a question and an AI agent instantly generates an accurate chart.
A small team manually investigated alerts across 35 AWS accounts. Now, engineers use AI chat to instantly pinpoint system anomalies.
Fragmented tools kept analysts chasing alerts until midnight. Now, AI agents resolve routine cases so teams work standard hours.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Surging calls caused long holds and overtime. A 24/7 AI voice agent handles routine payroll, freeing 700 HR partners for advisory work.
A 200% yearly data expansion bottlenecked global operations. Now, AI accelerates coding, drafts recipe cards, and resolves inquiries.
Legacy keyword search failed on typos and vague queries. Now, semantic AI interprets natural language and images to find exact items.